Overview

Dataset statistics

Number of variables18
Number of observations26749
Missing cells0
Missing cells (%)0.0%
Duplicate rows1431
Duplicate rows (%)5.3%
Total size in memory3.7 MiB
Average record size in memory144.0 B

Variable types

Numeric12
Categorical6

Warnings

Dataset has 1431 (5.3%) duplicate rows Duplicates
artists has a high cardinality: 11327 distinct values High cardinality
id has a high cardinality: 25318 distinct values High cardinality
name has a high cardinality: 22387 distinct values High cardinality
release_date has a high cardinality: 2689 distinct values High cardinality
id is uniformly distributed Uniform
name is uniformly distributed Uniform
instrumentalness has 8044 (30.1%) zeros Zeros
key has 2983 (11.2%) zeros Zeros

Reproduction

Analysis started2021-03-12 00:36:41.045410
Analysis finished2021-03-12 00:37:00.139107
Duration19.09 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

acousticness
Real number (ℝ≥0)

Distinct4317
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2255303994
Minimum0
Maximum0.996
Zeros6
Zeros (%)< 0.1%
Memory size209.1 KiB
2021-03-11T16:37:00.229872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.35 × 105
Q10.00455
median0.0658
Q30.36
95-th percentile0.902
Maximum0.996
Range0.996
Interquartile range (IQR)0.35545

Descriptive statistics

Standard deviation0.2996601914
Coefficient of variation (CV)1.328690909
Kurtosis0.2423220524
Mean0.2255303994
Median Absolute Deviation (MAD)0.065548
Skewness1.268799996
Sum6032.712653
Variance0.08979623033
MonotocityNot monotonic
2021-03-11T16:37:00.347134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10158
 
0.2%
0.31251
 
0.2%
0.11443
 
0.2%
0.22542
 
0.2%
0.12442
 
0.2%
0.11541
 
0.2%
0.11338
 
0.1%
0.10238
 
0.1%
0.16237
 
0.1%
0.10737
 
0.1%
Other values (4307)26322
98.4%
ValueCountFrequency (%)
06
< 0.1%
1.04 × 1062
 
< 0.1%
1.1 × 1063
< 0.1%
1.12 × 1061
 
< 0.1%
1.15 × 1061
 
< 0.1%
ValueCountFrequency (%)
0.99612
< 0.1%
0.99515
0.1%
0.99426
0.1%
0.99326
0.1%
0.99224
0.1%

artists
Categorical

HIGH CARDINALITY

Distinct11327
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Memory size209.1 KiB
['Armin van Buuren']
 
213
['Workout Music ']
 
203
['Taylor Swift']
 
151
['TandMProductionCo', 'TandMMusic', 'TandMTV']
 
150
['Roy Orbison', 'Alex Orbison', 'Chuck Turner']
 
146
Other values (11322)
25886 

Length

Max length242
Median length17
Mean length21.41137986
Min length5

Characters and Unicode

Total characters572733
Distinct characters323
Distinct categories17 ?
Distinct scripts8 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7915 ?
Unique (%)29.6%

Sample

1st row['Frank Sinatra']
2nd row['Frank Sinatra']
3rd row['Joni Mitchell']
4th row['Joni Mitchell']
5th row['Joni Mitchell']
ValueCountFrequency (%)
['Armin van Buuren']213
 
0.8%
['Workout Music ']203
 
0.8%
['Taylor Swift']151
 
0.6%
['TandMProductionCo', 'TandMMusic', 'TandMTV']150
 
0.6%
['Roy Orbison', 'Alex Orbison', 'Chuck Turner']146
 
0.5%
['Adam Gardner']99
 
0.4%
['Charttraxx Karaoke']96
 
0.4%
['David Bowie']94
 
0.4%
['Hollywood Session Group']91
 
0.3%
['Paul Carrack']89
 
0.3%
Other values (11317)25417
95.0%
2021-03-11T16:37:00.598500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the2132
 
3.0%
832
 
1.2%
van440
 
0.6%
dj383
 
0.5%
music382
 
0.5%
lil366
 
0.5%
armin350
 
0.5%
buuren349
 
0.5%
orbison299
 
0.4%
band281
 
0.4%
Other values (12055)65852
91.9%

Most occurring characters

ValueCountFrequency (%)
'73758
 
12.9%
44918
 
7.8%
e36159
 
6.3%
a33798
 
5.9%
[26752
 
4.7%
]26752
 
4.7%
n26188
 
4.6%
i25405
 
4.4%
o24566
 
4.3%
r24302
 
4.2%
Other values (313)230135
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter303083
52.9%
Other Punctuation86690
 
15.1%
Uppercase Letter79687
 
13.9%
Space Separator44918
 
7.8%
Open Punctuation26832
 
4.7%
Close Punctuation26832
 
4.7%
Decimal Number2019
 
0.4%
Other Letter1483
 
0.3%
Dash Punctuation548
 
0.1%
Nonspacing Mark340
 
0.1%
Other values (7)301
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
143
 
9.6%
139
 
9.4%
124
 
8.4%
106
 
7.1%
95
 
6.4%
78
 
5.3%
69
 
4.7%
67
 
4.5%
55
 
3.7%
50
 
3.4%
Other values (150)557
37.6%
ValueCountFrequency (%)
e36159
11.9%
a33798
11.2%
n26188
 
8.6%
i25405
 
8.4%
o24566
 
8.1%
r24302
 
8.0%
l17483
 
5.8%
s15642
 
5.2%
t15160
 
5.0%
u10879
 
3.6%
Other values (54)73501
24.3%
ValueCountFrequency (%)
T6573
 
8.2%
S6453
 
8.1%
M6298
 
7.9%
B5756
 
7.2%
A5266
 
6.6%
C5219
 
6.5%
D4507
 
5.7%
R4088
 
5.1%
J3854
 
4.8%
L3638
 
4.6%
Other values (37)28035
35.2%
ValueCountFrequency (%)
'73758
85.1%
,10360
 
12.0%
.1163
 
1.3%
&603
 
0.7%
"533
 
0.6%
!145
 
0.2%
/84
 
0.1%
:14
 
< 0.1%
@9
 
< 0.1%
%7
 
< 0.1%
Other values (4)14
 
< 0.1%
ValueCountFrequency (%)
133
39.1%
90
26.5%
59
17.4%
15
 
4.4%
13
 
3.8%
12
 
3.5%
6
 
1.8%
5
 
1.5%
3
 
0.9%
3
 
0.9%
ValueCountFrequency (%)
0457
22.6%
2374
18.5%
1295
14.6%
9160
 
7.9%
5155
 
7.7%
8149
 
7.4%
3136
 
6.7%
4130
 
6.4%
7105
 
5.2%
658
 
2.9%
ValueCountFrequency (%)
[26752
99.7%
(80
 
0.3%
ValueCountFrequency (%)
]26752
99.7%
)80
 
0.3%
ValueCountFrequency (%)
-546
99.6%
2
 
0.4%
ValueCountFrequency (%)
®2
66.7%
1
33.3%
ValueCountFrequency (%)
11
84.6%
2
 
15.4%
ValueCountFrequency (%)
+28
90.3%
|3
 
9.7%
ValueCountFrequency (%)
44918
100.0%
ValueCountFrequency (%)
$243
100.0%
ValueCountFrequency (%)
2
100.0%
ValueCountFrequency (%)
_8
100.0%
ValueCountFrequency (%)
²1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin382725
66.8%
Common188140
32.8%
Thai1693
 
0.3%
Han82
 
< 0.1%
Cyrillic45
 
< 0.1%
Hangul19
 
< 0.1%
Katakana16
 
< 0.1%
Hiragana13
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e36159
 
9.4%
a33798
 
8.8%
n26188
 
6.8%
i25405
 
6.6%
o24566
 
6.4%
r24302
 
6.3%
l17483
 
4.6%
s15642
 
4.1%
t15160
 
4.0%
u10879
 
2.8%
Other values (76)153143
40.0%
ValueCountFrequency (%)
2
 
2.4%
2
 
2.4%
2
 
2.4%
1
 
1.2%
1
 
1.2%
1
 
1.2%
1
 
1.2%
1
 
1.2%
1
 
1.2%
1
 
1.2%
Other values (69)69
84.1%
ValueCountFrequency (%)
143
 
8.4%
139
 
8.2%
133
 
7.9%
124
 
7.3%
106
 
6.3%
95
 
5.6%
90
 
5.3%
78
 
4.6%
69
 
4.1%
67
 
4.0%
Other values (38)649
38.3%
ValueCountFrequency (%)
'73758
39.2%
44918
23.9%
[26752
 
14.2%
]26752
 
14.2%
,10360
 
5.5%
.1163
 
0.6%
&603
 
0.3%
-546
 
0.3%
"533
 
0.3%
0457
 
0.2%
Other values (31)2298
 
1.2%
ValueCountFrequency (%)
е5
 
11.1%
о4
 
8.9%
а4
 
8.9%
з3
 
6.7%
Ч2
 
4.4%
б2
 
4.4%
П2
 
4.4%
т2
 
4.4%
р2
 
4.4%
н2
 
4.4%
Other values (15)17
37.8%
ValueCountFrequency (%)
2
 
10.5%
2
 
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (7)7
36.8%
ValueCountFrequency (%)
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2
15.4%
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII570184
99.6%
Thai1693
 
0.3%
None664
 
0.1%
CJK82
 
< 0.1%
Cyrillic45
 
< 0.1%
Hangul19
 
< 0.1%
Katakana18
 
< 0.1%
Punctuation14
 
< 0.1%
Hiragana13
 
< 0.1%
Misc Symbols1
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
'73758
 
12.9%
44918
 
7.9%
e36159
 
6.3%
a33798
 
5.9%
[26752
 
4.7%
]26752
 
4.7%
n26188
 
4.6%
i25405
 
4.5%
o24566
 
4.3%
r24302
 
4.3%
Other values (75)227586
39.9%
ValueCountFrequency (%)
é261
39.3%
á80
 
12.0%
ö49
 
7.4%
ä32
 
4.8%
ç28
 
4.2%
ú28
 
4.2%
ü22
 
3.3%
å22
 
3.3%
ë21
 
3.2%
ó19
 
2.9%
Other values (27)102
 
15.4%
ValueCountFrequency (%)
143
 
8.4%
139
 
8.2%
133
 
7.9%
124
 
7.3%
106
 
6.3%
95
 
5.6%
90
 
5.3%
78
 
4.6%
69
 
4.1%
67
 
4.0%
Other values (38)649
38.3%
ValueCountFrequency (%)
11
78.6%
2
 
14.3%
1
 
7.1%
ValueCountFrequency (%)
2
 
10.5%
2
 
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (7)7
36.8%
ValueCountFrequency (%)
2
 
2.4%
2
 
2.4%
2
 
2.4%
1
 
1.2%
1
 
1.2%
1
 
1.2%
1
 
1.2%
1
 
1.2%
1
 
1.2%
1
 
1.2%
Other values (69)69
84.1%
ValueCountFrequency (%)
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other values (7)7
38.9%
ValueCountFrequency (%)
е5
 
11.1%
о4
 
8.9%
а4
 
8.9%
з3
 
6.7%
Ч2
 
4.4%
б2
 
4.4%
П2
 
4.4%
т2
 
4.4%
р2
 
4.4%
н2
 
4.4%
Other values (15)17
37.8%
ValueCountFrequency (%)
2
15.4%
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
ValueCountFrequency (%)
1
100.0%

danceability
Real number (ℝ≥0)

Distinct967
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5884334405
Minimum0
Maximum0.986
Zeros41
Zeros (%)0.2%
Memory size209.1 KiB
2021-03-11T16:37:00.715673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.272
Q10.479
median0.599
Q30.718
95-th percentile0.843
Maximum0.986
Range0.986
Interquartile range (IQR)0.239

Descriptive statistics

Standard deviation0.1732731783
Coefficient of variation (CV)0.2944652128
Kurtosis-0.03826221572
Mean0.5884334405
Median Absolute Deviation (MAD)0.119
Skewness-0.4436758264
Sum15740.0061
Variance0.03002359431
MonotocityNot monotonic
2021-03-11T16:37:00.824805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67588
 
0.3%
0.65485
 
0.3%
0.59983
 
0.3%
0.61482
 
0.3%
0.65182
 
0.3%
0.49581
 
0.3%
0.67180
 
0.3%
0.62879
 
0.3%
0.54779
 
0.3%
0.80878
 
0.3%
Other values (957)25932
96.9%
ValueCountFrequency (%)
041
0.2%
0.05511
 
< 0.1%
0.05592
 
< 0.1%
0.05741
 
< 0.1%
0.05861
 
< 0.1%
ValueCountFrequency (%)
0.9861
 
< 0.1%
0.9853
< 0.1%
0.9821
 
< 0.1%
0.981
 
< 0.1%
0.9753
< 0.1%

duration_ms
Real number (ℝ≥0)

Distinct19282
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250088.7271
Minimum4937
Maximum5338302
Zeros0
Zeros (%)0.0%
Memory size209.1 KiB
2021-03-11T16:37:00.935977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4937
5-th percentile79693.6
Q1182720
median222093
Q3276772
95-th percentile454473.2
Maximum5338302
Range5333365
Interquartile range (IQR)94052

Descriptive statistics

Standard deviation216250.5807
Coefficient of variation (CV)0.8646954351
Kurtosis219.2182467
Mean250088.7271
Median Absolute Deviation (MAD)45294
Skewness12.71520995
Sum6689623361
Variance4.676431365 × 1010
MonotocityNot monotonic
2021-03-11T16:37:01.047588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25300039
 
0.1%
4900034
 
0.1%
15000031
 
0.1%
34312531
 
0.1%
36000024
 
0.1%
24000019
 
0.1%
26400018
 
0.1%
21360017
 
0.1%
18000016
 
0.1%
22800016
 
0.1%
Other values (19272)26504
99.1%
ValueCountFrequency (%)
49371
< 0.1%
80421
< 0.1%
120001
< 0.1%
127591
< 0.1%
132841
< 0.1%
ValueCountFrequency (%)
53383021
< 0.1%
50421851
< 0.1%
48001182
< 0.1%
47925871
< 0.1%
47374581
< 0.1%

energy
Real number (ℝ≥0)

Distinct1310
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6607594601
Minimum2.01 × 105
Maximum1
Zeros0
Zeros (%)0.0%
Memory size209.1 KiB
2021-03-11T16:37:01.158139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.01 × 105
5-th percentile0.208
Q10.506
median0.695
Q30.857
95-th percentile0.973
Maximum1
Range0.9999799
Interquartile range (IQR)0.351

Descriptive statistics

Standard deviation0.2357790534
Coefficient of variation (CV)0.3568303863
Kurtosis-0.2997741321
Mean0.6607594601
Median Absolute Deviation (MAD)0.173
Skewness-0.6335123128
Sum17674.6548
Variance0.05559176203
MonotocityNot monotonic
2021-03-11T16:37:01.268392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.72678
 
0.3%
0.97975
 
0.3%
0.85668
 
0.3%
0.69168
 
0.3%
0.9567
 
0.3%
0.9766
 
0.2%
0.86865
 
0.2%
0.96163
 
0.2%
0.94963
 
0.2%
0.87262
 
0.2%
Other values (1300)26074
97.5%
ValueCountFrequency (%)
2.01 × 1053
< 0.1%
2.03 × 1053
< 0.1%
4.28 × 1051
 
< 0.1%
4.98 × 1051
 
< 0.1%
6.19 × 1052
< 0.1%
ValueCountFrequency (%)
117
 
0.1%
0.99951
0.2%
0.99861
0.2%
0.99759
0.2%
0.99652
0.2%

explicit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size209.1 KiB
0
22186 
1
4563 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26749
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
022186
82.9%
14563
 
17.1%
2021-03-11T16:37:01.450256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-11T16:37:01.694330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
022186
82.9%
14563
 
17.1%

Most occurring characters

ValueCountFrequency (%)
022186
82.9%
14563
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26749
100.0%

Most frequent character per category

ValueCountFrequency (%)
022186
82.9%
14563
 
17.1%

Most occurring scripts

ValueCountFrequency (%)
Common26749
100.0%

Most frequent character per script

ValueCountFrequency (%)
022186
82.9%
14563
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII26749
100.0%

Most frequent character per block

ValueCountFrequency (%)
022186
82.9%
14563
 
17.1%

id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct25318
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size209.1 KiB
0UsmyJDsst2xhX1ZiFF3JW
 
9
7tH6tGz6cQtpYReqHTlyjN
 
8
7Kh32CyazzTdVEBXjKINVO
 
8
58lQgf1Y5gRJRr6S0Nwp50
 
7
7FmPp4BjDVaEwhsEO7B7Oe
 
7
Other values (25313)
26710 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters588478
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24033 ?
Unique (%)89.8%

Sample

1st row1hx7X9cMXHWJjknb9O6Ava
2nd row19oquvXf3bc65GSqtPYA5S
3rd row55qyghODi24yaDgKBI6lx0
4th row00xemFYjQNRpOlPhVaLAHa
5th row2lm5FQJRHvc3rUN5YHpEWj
ValueCountFrequency (%)
0UsmyJDsst2xhX1ZiFF3JW9
 
< 0.1%
7tH6tGz6cQtpYReqHTlyjN8
 
< 0.1%
7Kh32CyazzTdVEBXjKINVO8
 
< 0.1%
58lQgf1Y5gRJRr6S0Nwp507
 
< 0.1%
7FmPp4BjDVaEwhsEO7B7Oe7
 
< 0.1%
00ohIpPn9LkKpeIqhfIU9V7
 
< 0.1%
5rJw9VsPNdfnV9Ar97xZG27
 
< 0.1%
4KoDsg2EbYPgqpXSrKqB7F6
 
< 0.1%
0QXEvVJyHIQhVzci6kBULo6
 
< 0.1%
0Io1kinEi9fp1b4Maf84yO6
 
< 0.1%
Other values (25308)26678
99.7%
2021-03-11T16:37:01.933712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0usmyjdsst2xhx1ziff3jw9
 
< 0.1%
7th6tgz6cqtpyreqhtlyjn8
 
< 0.1%
7kh32cyazztdvebxjkinvo8
 
< 0.1%
00ohippn9lkkpeiqhfiu9v7
 
< 0.1%
7fmpp4bjdvaewhseo7b7oe7
 
< 0.1%
5rjw9vspndfnv9ar97xzg27
 
< 0.1%
58lqgf1y5grjrr6s0nwp507
 
< 0.1%
0io1kinei9fp1b4maf84yo6
 
< 0.1%
0qxevvjyhiqhvzci6kbulo6
 
< 0.1%
0sbb2st9txikbkqw7xuzrw6
 
< 0.1%
Other values (25308)26678
99.7%

Most occurring characters

ValueCountFrequency (%)
012612
 
2.1%
612558
 
2.1%
512548
 
2.1%
412418
 
2.1%
312416
 
2.1%
212413
 
2.1%
112304
 
2.1%
711772
 
2.0%
s9253
 
1.6%
d9243
 
1.6%
Other values (52)470941
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter235835
40.1%
Uppercase Letter235546
40.0%
Decimal Number117097
19.9%

Most frequent character per category

ValueCountFrequency (%)
s9253
 
3.9%
d9243
 
3.9%
t9181
 
3.9%
q9178
 
3.9%
f9160
 
3.9%
r9145
 
3.9%
y9144
 
3.9%
h9141
 
3.9%
b9133
 
3.9%
p9129
 
3.9%
Other values (16)144128
61.1%
ValueCountFrequency (%)
K9198
 
3.9%
F9187
 
3.9%
T9178
 
3.9%
X9177
 
3.9%
J9169
 
3.9%
S9167
 
3.9%
B9152
 
3.9%
L9141
 
3.9%
D9137
 
3.9%
C9121
 
3.9%
Other values (16)143919
61.1%
ValueCountFrequency (%)
012612
10.8%
612558
10.7%
512548
10.7%
412418
10.6%
312416
10.6%
212413
10.6%
112304
10.5%
711772
10.1%
99049
7.7%
89007
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin471381
80.1%
Common117097
 
19.9%

Most frequent character per script

ValueCountFrequency (%)
s9253
 
2.0%
d9243
 
2.0%
K9198
 
2.0%
F9187
 
1.9%
t9181
 
1.9%
q9178
 
1.9%
T9178
 
1.9%
X9177
 
1.9%
J9169
 
1.9%
S9167
 
1.9%
Other values (42)379450
80.5%
ValueCountFrequency (%)
012612
10.8%
612558
10.7%
512548
10.7%
412418
10.6%
312416
10.6%
212413
10.6%
112304
10.5%
711772
10.1%
99049
7.7%
89007
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII588478
100.0%

Most frequent character per block

ValueCountFrequency (%)
012612
 
2.1%
612558
 
2.1%
512548
 
2.1%
412418
 
2.1%
312416
 
2.1%
212413
 
2.1%
112304
 
2.1%
711772
 
2.0%
s9253
 
1.6%
d9243
 
1.6%
Other values (52)470941
80.0%

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct4379
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2349304515
Minimum0
Maximum1
Zeros8044
Zeros (%)30.1%
Memory size209.1 KiB
2021-03-11T16:37:02.030796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000452
Q30.531
95-th percentile0.92
Maximum1
Range1
Interquartile range (IQR)0.531

Descriptive statistics

Standard deviation0.3600561842
Coefficient of variation (CV)1.532607552
Kurtosis-0.6421174068
Mean0.2349304515
Median Absolute Deviation (MAD)0.000452
Skewness1.08064318
Sum6284.154647
Variance0.1296404558
MonotocityNot monotonic
2021-03-11T16:37:02.144078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08044
30.1%
0.91165
 
0.2%
0.87352
 
0.2%
0.90151
 
0.2%
0.9145
 
0.2%
0.90545
 
0.2%
0.89743
 
0.2%
0.90742
 
0.2%
0.90242
 
0.2%
0.91741
 
0.2%
Other values (4369)18279
68.3%
ValueCountFrequency (%)
08044
30.1%
1 × 1066
 
< 0.1%
1.01 × 1069
 
< 0.1%
1.02 × 1069
 
< 0.1%
1.03 × 1069
 
< 0.1%
ValueCountFrequency (%)
17
< 0.1%
0.9994
< 0.1%
0.9985
< 0.1%
0.9974
< 0.1%
0.9965
< 0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.327601032
Minimum0
Maximum11
Zeros2983
Zeros (%)11.2%
Memory size209.1 KiB
2021-03-11T16:37:02.239117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.590069426
Coefficient of variation (CV)0.6738622889
Kurtosis-1.298771024
Mean5.327601032
Median Absolute Deviation (MAD)3
Skewness-0.01421274094
Sum142508
Variance12.88859848
MonotocityNot monotonic
2021-03-11T16:37:02.312343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
73102
11.6%
02983
11.2%
12886
10.8%
92672
10.0%
22584
9.7%
112263
8.5%
42067
7.7%
52038
7.6%
61980
7.4%
101807
6.8%
Other values (2)2367
8.8%
ValueCountFrequency (%)
02983
11.2%
12886
10.8%
22584
9.7%
3709
 
2.7%
42067
7.7%
ValueCountFrequency (%)
112263
8.5%
101807
6.8%
92672
10.0%
81658
6.2%
73102
11.6%

liveness
Real number (ℝ≥0)

Distinct1633
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.220280717
Minimum0
Maximum0.993
Zeros3
Zeros (%)< 0.1%
Memory size209.1 KiB
2021-03-11T16:37:02.408489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0585
Q10.0967
median0.133
Q30.294
95-th percentile0.687
Maximum0.993
Range0.993
Interquartile range (IQR)0.1973

Descriptive statistics

Standard deviation0.1950971367
Coefficient of variation (CV)0.8856750576
Kurtosis3.229991072
Mean0.220280717
Median Absolute Deviation (MAD)0.055
Skewness1.8760515
Sum5892.2889
Variance0.03806289277
MonotocityNot monotonic
2021-03-11T16:37:02.515748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111311
 
1.2%
0.11268
 
1.0%
0.109263
 
1.0%
0.112259
 
1.0%
0.106257
 
1.0%
0.107257
 
1.0%
0.108252
 
0.9%
0.105246
 
0.9%
0.102235
 
0.9%
0.103216
 
0.8%
Other values (1623)24185
90.4%
ValueCountFrequency (%)
03
< 0.1%
0.01191
 
< 0.1%
0.01421
 
< 0.1%
0.01471
 
< 0.1%
0.01641
 
< 0.1%
ValueCountFrequency (%)
0.9932
< 0.1%
0.9922
< 0.1%
0.9914
< 0.1%
0.993
< 0.1%
0.9883
< 0.1%

loudness
Real number (ℝ)

Distinct10965
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.176739766
Minimum-54.376
Maximum3.367
Zeros0
Zeros (%)0.0%
Memory size209.1 KiB
2021-03-11T16:37:02.622076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-54.376
5-th percentile-16.6002
Q1-9.643
median-7.146
Q3-5.403
95-th percentile-3.362
Maximum3.367
Range57.743
Interquartile range (IQR)4.24

Descriptive statistics

Standard deviation4.471252872
Coefficient of variation (CV)-0.5468258744
Kurtosis8.220659585
Mean-8.176739766
Median Absolute Deviation (MAD)2.004
Skewness-2.230982979
Sum-218719.612
Variance19.99210225
MonotocityNot monotonic
2021-03-11T16:37:02.720211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.57833
 
0.1%
-3.30432
 
0.1%
-8.70528
 
0.1%
-7.03122
 
0.1%
-5.70221
 
0.1%
-10.43816
 
0.1%
-12.55215
 
0.1%
-6.59414
 
0.1%
-7.25614
 
0.1%
-2.09313
 
< 0.1%
Other values (10955)26541
99.2%
ValueCountFrequency (%)
-54.3761
< 0.1%
-47.7311
< 0.1%
-45.3531
< 0.1%
-44.7611
< 0.1%
-44.2811
< 0.1%
ValueCountFrequency (%)
3.3671
 
< 0.1%
1.0275
< 0.1%
1.0231
 
< 0.1%
0.9771
 
< 0.1%
0.8991
 
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size209.1 KiB
1
16700 
0
10049 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26749
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
116700
62.4%
010049
37.6%
2021-03-11T16:37:02.895982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-11T16:37:02.945757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
116700
62.4%
010049
37.6%

Most occurring characters

ValueCountFrequency (%)
116700
62.4%
010049
37.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26749
100.0%

Most frequent character per category

ValueCountFrequency (%)
116700
62.4%
010049
37.6%

Most occurring scripts

ValueCountFrequency (%)
Common26749
100.0%

Most frequent character per script

ValueCountFrequency (%)
116700
62.4%
010049
37.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII26749
100.0%

Most frequent character per block

ValueCountFrequency (%)
116700
62.4%
010049
37.6%

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct22387
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Memory size209.1 KiB
Happy New Year
 
50
2000 Years
 
49
99 Year Blues
 
44
7 Years
 
36
My Only Wish (This Year)
 
36
Other values (22382)
26534 

Length

Max length197
Median length19
Mean length22.94149314
Min length1

Characters and Unicode

Total characters613662
Distinct characters527
Distinct categories18 ?
Distinct scripts10 ?
Distinct blocks13 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19552 ?
Unique (%)73.1%

Sample

1st rowThe September Of My Years - Live At The Sands Hotel And Casino, Las Vegas/1966 / Show 2
2nd rowIt Was A Very Good Year - Live At The Sands Hotel And Casino, Las Vegas/1966 / Show 2
3rd rowThe Circle Game - Live at The 2nd Fret, Philadelphia, PA, 11/1966
4th rowUrge For Going - Live at The 2nd Fret, Philadelphia, PA, 11/1966
5th rowWhat's The Story Mr. Blue - Live at The 2nd Fret, Philadelphia, PA, 11/1966
ValueCountFrequency (%)
Happy New Year50
 
0.2%
2000 Years49
 
0.2%
99 Year Blues44
 
0.2%
7 Years36
 
0.1%
My Only Wish (This Year)36
 
0.1%
It's the Most Wonderful Time of the Year33
 
0.1%
New Years Day - Original Mix32
 
0.1%
New Year23
 
0.1%
Year 200019
 
0.1%
99 Year Blues - Live16
 
0.1%
Other values (22377)26411
98.7%
2021-03-11T16:37:03.195631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8613
 
7.5%
the3451
 
3.0%
mix2599
 
2.3%
year1793
 
1.6%
of1720
 
1.5%
remix1562
 
1.4%
feat1340
 
1.2%
you1198
 
1.0%
years1183
 
1.0%
a1170
 
1.0%
Other values (15088)90651
78.6%

Most occurring characters

ValueCountFrequency (%)
88531
 
14.4%
e54122
 
8.8%
a34245
 
5.6%
i31794
 
5.2%
o29610
 
4.8%
r27729
 
4.5%
n25924
 
4.2%
t25820
 
4.2%
s18978
 
3.1%
l16216
 
2.6%
Other values (517)260693
42.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter367140
59.8%
Uppercase Letter97626
 
15.9%
Space Separator88531
 
14.4%
Decimal Number27336
 
4.5%
Other Punctuation9756
 
1.6%
Dash Punctuation7919
 
1.3%
Close Punctuation5669
 
0.9%
Open Punctuation5667
 
0.9%
Other Letter2937
 
0.5%
Nonspacing Mark598
 
0.1%
Other values (8)483
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
196
 
6.7%
162
 
5.5%
160
 
5.4%
147
 
5.0%
142
 
4.8%
130
 
4.4%
129
 
4.4%
100
 
3.4%
98
 
3.3%
90
 
3.1%
Other values (284)1583
53.9%
ValueCountFrequency (%)
e54122
14.7%
a34245
 
9.3%
i31794
 
8.7%
o29610
 
8.1%
r27729
 
7.6%
n25924
 
7.1%
t25820
 
7.0%
s18978
 
5.2%
l16216
 
4.4%
h12341
 
3.4%
Other values (89)90361
24.6%
ValueCountFrequency (%)
M8625
 
8.8%
T7735
 
7.9%
S7214
 
7.4%
R6796
 
7.0%
Y5792
 
5.9%
L5305
 
5.4%
A5235
 
5.4%
B4962
 
5.1%
O4860
 
5.0%
C4823
 
4.9%
Other values (47)36279
37.2%
ValueCountFrequency (%)
.2988
30.6%
'2077
21.3%
,1906
19.5%
&704
 
7.2%
/584
 
6.0%
:420
 
4.3%
"396
 
4.1%
!229
 
2.3%
#158
 
1.6%
?113
 
1.2%
Other values (11)181
 
1.9%
ValueCountFrequency (%)
176
29.4%
105
17.6%
97
16.2%
69
 
11.5%
37
 
6.2%
25
 
4.2%
23
 
3.8%
22
 
3.7%
19
 
3.2%
14
 
2.3%
Other values (2)11
 
1.8%
ValueCountFrequency (%)
08511
31.1%
27006
25.6%
15029
18.4%
91374
 
5.0%
51153
 
4.2%
4917
 
3.4%
8886
 
3.2%
7847
 
3.1%
6841
 
3.1%
3772
 
2.8%
ValueCountFrequency (%)
+188
89.5%
~13
 
6.2%
|3
 
1.4%
>2
 
1.0%
=2
 
1.0%
<2
 
1.0%
ValueCountFrequency (%)
(5304
93.6%
[360
 
6.4%
1
 
< 0.1%
1
 
< 0.1%
{1
 
< 0.1%
ValueCountFrequency (%)
)5304
93.6%
]362
 
6.4%
1
 
< 0.1%
1
 
< 0.1%
}1
 
< 0.1%
ValueCountFrequency (%)
-7899
99.7%
10
 
0.1%
8
 
0.1%
2
 
< 0.1%
ValueCountFrequency (%)
°1
33.3%
1
33.3%
®1
33.3%
ValueCountFrequency (%)
134
93.7%
9
 
6.3%
ValueCountFrequency (%)
9
81.8%
2
 
18.2%
ValueCountFrequency (%)
15
78.9%
4
 
21.1%
ValueCountFrequency (%)
´5
83.3%
`1
 
16.7%
ValueCountFrequency (%)
88531
100.0%
ValueCountFrequency (%)
$83
100.0%
ValueCountFrequency (%)
_8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin464374
75.7%
Common145357
 
23.7%
Thai2980
 
0.5%
Cyrillic374
 
0.1%
Han173
 
< 0.1%
Katakana171
 
< 0.1%
Hiragana120
 
< 0.1%
Hangul94
 
< 0.1%
Georgian18
 
< 0.1%
Inherited1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e54122
 
11.7%
a34245
 
7.4%
i31794
 
6.8%
o29610
 
6.4%
r27729
 
6.0%
n25924
 
5.6%
t25820
 
5.6%
s18978
 
4.1%
l16216
 
3.5%
h12341
 
2.7%
Other values (92)187595
40.4%
ValueCountFrequency (%)
14
 
8.1%
14
 
8.1%
14
 
8.1%
14
 
8.1%
4
 
2.3%
4
 
2.3%
4
 
2.3%
3
 
1.7%
3
 
1.7%
3
 
1.7%
Other values (79)96
55.5%
ValueCountFrequency (%)
4
 
4.3%
4
 
4.3%
3
 
3.2%
3
 
3.2%
3
 
3.2%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
Other values (57)67
71.3%
ValueCountFrequency (%)
88531
60.9%
08511
 
5.9%
-7899
 
5.4%
27006
 
4.8%
(5304
 
3.6%
)5304
 
3.6%
15029
 
3.5%
.2988
 
2.1%
'2077
 
1.4%
,1906
 
1.3%
Other values (54)10802
 
7.4%
ValueCountFrequency (%)
196
 
6.6%
176
 
5.9%
162
 
5.4%
160
 
5.4%
147
 
4.9%
142
 
4.8%
130
 
4.4%
129
 
4.3%
105
 
3.5%
100
 
3.4%
Other values (47)1533
51.4%
ValueCountFrequency (%)
16
 
9.4%
11
 
6.4%
9
 
5.3%
9
 
5.3%
7
 
4.1%
7
 
4.1%
6
 
3.5%
5
 
2.9%
5
 
2.9%
5
 
2.9%
Other values (40)91
53.2%
ValueCountFrequency (%)
а43
 
11.5%
н28
 
7.5%
о27
 
7.2%
и26
 
7.0%
е25
 
6.7%
р18
 
4.8%
к16
 
4.3%
д13
 
3.5%
л12
 
3.2%
с11
 
2.9%
Other values (38)155
41.4%
ValueCountFrequency (%)
14
 
11.7%
8
 
6.7%
6
 
5.0%
6
 
5.0%
5
 
4.2%
5
 
4.2%
5
 
4.2%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (33)59
49.2%
ValueCountFrequency (%)
6
33.3%
4
22.2%
2
 
11.1%
2
 
11.1%
2
 
11.1%
2
 
11.1%
ValueCountFrequency (%)
́1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII608989
99.2%
Thai2980
 
0.5%
None547
 
0.1%
Cyrillic374
 
0.1%
Katakana193
 
< 0.1%
CJK173
 
< 0.1%
Punctuation170
 
< 0.1%
Hiragana120
 
< 0.1%
Hangul94
 
< 0.1%
Georgian18
 
< 0.1%
Other values (3)4
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
88531
 
14.5%
e54122
 
8.9%
a34245
 
5.6%
i31794
 
5.2%
o29610
 
4.9%
r27729
 
4.6%
n25924
 
4.3%
t25820
 
4.2%
s18978
 
3.1%
l16216
 
2.7%
Other values (84)256020
42.0%
ValueCountFrequency (%)
é111
20.3%
á46
 
8.4%
ü42
 
7.7%
ó35
 
6.4%
ä30
 
5.5%
í30
 
5.5%
ö26
 
4.8%
ç25
 
4.6%
ñ22
 
4.0%
ú20
 
3.7%
Other values (49)160
29.3%
ValueCountFrequency (%)
134
78.8%
9
 
5.3%
9
 
5.3%
8
 
4.7%
4
 
2.4%
2
 
1.2%
2
 
1.2%
2
 
1.2%
ValueCountFrequency (%)
196
 
6.6%
176
 
5.9%
162
 
5.4%
160
 
5.4%
147
 
4.9%
142
 
4.8%
130
 
4.4%
129
 
4.3%
105
 
3.5%
100
 
3.4%
Other values (47)1533
51.4%
ValueCountFrequency (%)
14
 
8.1%
14
 
8.1%
14
 
8.1%
14
 
8.1%
4
 
2.3%
4
 
2.3%
4
 
2.3%
3
 
1.7%
3
 
1.7%
3
 
1.7%
Other values (79)96
55.5%
ValueCountFrequency (%)
14
 
11.7%
8
 
6.7%
6
 
5.0%
6
 
5.0%
5
 
4.2%
5
 
4.2%
5
 
4.2%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (33)59
49.2%
ValueCountFrequency (%)
4
 
4.3%
4
 
4.3%
3
 
3.2%
3
 
3.2%
3
 
3.2%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
Other values (57)67
71.3%
ValueCountFrequency (%)
а43
 
11.5%
н28
 
7.5%
о27
 
7.2%
и26
 
7.0%
е25
 
6.7%
р18
 
4.8%
к16
 
4.3%
д13
 
3.5%
л12
 
3.2%
с11
 
2.9%
Other values (38)155
41.4%
ValueCountFrequency (%)
16
 
8.3%
15
 
7.8%
11
 
5.7%
9
 
4.7%
9
 
4.7%
7
 
3.6%
7
 
3.6%
7
 
3.6%
6
 
3.1%
5
 
2.6%
Other values (42)101
52.3%
ValueCountFrequency (%)
1
50.0%
1
50.0%
ValueCountFrequency (%)
6
33.3%
4
22.2%
2
 
11.1%
2
 
11.1%
2
 
11.1%
2
 
11.1%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
́1
100.0%

release_date
Categorical

HIGH CARDINALITY

Distinct2689
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size209.1 KiB
2020-12-11
 
346
2010-01-01
 
340
2011-01-01
 
316
2013-01-01
 
287
2012-01-01
 
274
Other values (2684)
25186 

Length

Max length10
Median length10
Mean length9.831993719
Min length4

Characters and Unicode

Total characters262996
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique717 ?
Unique (%)2.7%

Sample

1st row2018-05-04
2nd row2018-05-04
3rd row2020-10-30
4th row2020-10-30
5th row2020-10-30
ValueCountFrequency (%)
2020-12-11346
 
1.3%
2010-01-01340
 
1.3%
2011-01-01316
 
1.2%
2013-01-01287
 
1.1%
2012-01-01274
 
1.0%
2019-12-13260
 
1.0%
2017-12-15227
 
0.8%
2014-12-19192
 
0.7%
2018-12-14185
 
0.7%
2020-12-23182
 
0.7%
Other values (2679)24140
90.2%
2021-03-11T16:37:03.440714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-11346
 
1.3%
2010-01-01340
 
1.3%
2011-01-01316
 
1.2%
2013-01-01287
 
1.1%
2012-01-01274
 
1.0%
2019-12-13260
 
1.0%
2017-12-15227
 
0.8%
2014-12-19192
 
0.7%
2018-12-14185
 
0.7%
2020-12-23182
 
0.7%
Other values (2679)24140
90.2%

Most occurring characters

ValueCountFrequency (%)
060833
23.1%
154366
20.7%
-52000
19.8%
250075
19.0%
37914
 
3.0%
66674
 
2.5%
96637
 
2.5%
56479
 
2.5%
86345
 
2.4%
45840
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number210996
80.2%
Dash Punctuation52000
 
19.8%

Most frequent character per category

ValueCountFrequency (%)
060833
28.8%
154366
25.8%
250075
23.7%
37914
 
3.8%
66674
 
3.2%
96637
 
3.1%
56479
 
3.1%
86345
 
3.0%
45840
 
2.8%
75833
 
2.8%
ValueCountFrequency (%)
-52000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common262996
100.0%

Most frequent character per script

ValueCountFrequency (%)
060833
23.1%
154366
20.7%
-52000
19.8%
250075
19.0%
37914
 
3.0%
66674
 
2.5%
96637
 
2.5%
56479
 
2.5%
86345
 
2.4%
45840
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII262996
100.0%

Most frequent character per block

ValueCountFrequency (%)
060833
23.1%
154366
20.7%
-52000
19.8%
250075
19.0%
37914
 
3.0%
66674
 
2.5%
96637
 
2.5%
56479
 
2.5%
86345
 
2.4%
45840
 
2.2%

speechiness
Real number (ℝ≥0)

Distinct1373
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09536206961
Minimum0
Maximum0.962
Zeros41
Zeros (%)0.2%
Memory size209.1 KiB
2021-03-11T16:37:03.535965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0285
Q10.0377
median0.0534
Q30.0973
95-th percentile0.324
Maximum0.962
Range0.962
Interquartile range (IQR)0.0596

Descriptive statistics

Standard deviation0.1099780594
Coefficient of variation (CV)1.153268379
Kurtosis15.91914327
Mean0.09536206961
Median Absolute Deviation (MAD)0.0198
Skewness3.403954661
Sum2550.84
Variance0.01209517355
MonotocityNot monotonic
2021-03-11T16:37:03.653438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.031881
 
0.3%
0.036281
 
0.3%
0.032479
 
0.3%
0.033778
 
0.3%
0.033476
 
0.3%
0.030873
 
0.3%
0.031673
 
0.3%
0.029972
 
0.3%
0.045772
 
0.3%
0.10271
 
0.3%
Other values (1363)25993
97.2%
ValueCountFrequency (%)
041
0.2%
0.02241
 
< 0.1%
0.02261
 
< 0.1%
0.02271
 
< 0.1%
0.02292
 
< 0.1%
ValueCountFrequency (%)
0.9623
< 0.1%
0.9612
< 0.1%
0.961
 
< 0.1%
0.9591
 
< 0.1%
0.9582
< 0.1%

tempo
Real number (ℝ≥0)

Distinct16188
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.3657748
Minimum0
Maximum221.954
Zeros41
Zeros (%)0.2%
Memory size209.1 KiB
2021-03-11T16:37:03.774056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79.1362
Q1104.39
median126.031
Q3138.041
95-th percentile171.558
Maximum221.954
Range221.954
Interquartile range (IQR)33.651

Descriptive statistics

Standard deviation26.9124751
Coefficient of variation (CV)0.2181518752
Kurtosis0.5419374643
Mean123.3657748
Median Absolute Deviation (MAD)15.282
Skewness0.001004568883
Sum3299911.109
Variance724.2813161
MonotocityNot monotonic
2021-03-11T16:37:03.877465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.00352
 
0.2%
041
 
0.2%
127.99837
 
0.1%
12836
 
0.1%
128.00836
 
0.1%
130.00736
 
0.1%
128.01335
 
0.1%
128.0134
 
0.1%
127.99133
 
0.1%
127.99232
 
0.1%
Other values (16178)26377
98.6%
ValueCountFrequency (%)
041
0.2%
35.7021
 
< 0.1%
36.971
 
< 0.1%
43.5091
 
< 0.1%
46.181
 
< 0.1%
ValueCountFrequency (%)
221.9541
< 0.1%
220.0991
< 0.1%
219.9711
< 0.1%
219.861
< 0.1%
219.8331
< 0.1%

valence
Real number (ℝ≥0)

Distinct1552
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4525039916
Minimum0
Maximum0.997
Zeros43
Zeros (%)0.2%
Memory size209.1 KiB
2021-03-11T16:37:03.984976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0688
Q10.249
median0.438
Q30.643
95-th percentile0.883
Maximum0.997
Range0.997
Interquartile range (IQR)0.394

Descriptive statistics

Standard deviation0.2488990754
Coefficient of variation (CV)0.5500483533
Kurtosis-0.9168295398
Mean0.4525039916
Median Absolute Deviation (MAD)0.197
Skewness0.1842376677
Sum12104.02927
Variance0.06195074975
MonotocityNot monotonic
2021-03-11T16:37:04.097773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3557
 
0.2%
0.3456
 
0.2%
0.22754
 
0.2%
0.45153
 
0.2%
0.38553
 
0.2%
0.35152
 
0.2%
0.29452
 
0.2%
0.66151
 
0.2%
0.12951
 
0.2%
0.39851
 
0.2%
Other values (1542)26219
98.0%
ValueCountFrequency (%)
043
0.2%
1 × 10528
0.1%
0.001731
 
< 0.1%
0.002281
 
< 0.1%
0.002981
 
< 0.1%
ValueCountFrequency (%)
0.9971
 
< 0.1%
0.991
 
< 0.1%
0.9833
< 0.1%
0.9822
< 0.1%
0.9813
< 0.1%

year
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.566713
Minimum2010
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size209.1 KiB
2021-03-11T16:37:04.193904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12013
median2016
Q32018
95-th percentile2020
Maximum2020
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.234435768
Coefficient of variation (CV)0.001604727716
Kurtosis-1.222648871
Mean2015.566713
Median Absolute Deviation (MAD)3
Skewness-0.1538197645
Sum53914394
Variance10.46157474
MonotocityNot monotonic
2021-03-11T16:37:04.270949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
20204294
16.1%
20182714
10.1%
20132622
9.8%
20162349
8.8%
20192329
8.7%
20152300
8.6%
20142252
8.4%
20172156
8.1%
20101961
7.3%
20121959
7.3%
ValueCountFrequency (%)
20101961
7.3%
20111813
6.8%
20121959
7.3%
20132622
9.8%
20142252
8.4%
ValueCountFrequency (%)
20204294
16.1%
20192329
8.7%
20182714
10.1%
20172156
8.1%
20162349
8.8%

Interactions

2021-03-11T16:36:46.337343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:46.466507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:46.572607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:46.684824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:46.802877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:46.921110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:47.037320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:47.147929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:47.249676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:47.351328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:47.453197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:47.553678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:47.669314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:47.781883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:47.893517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:47.990883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:48.082345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:48.173424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:48.279357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:48.394631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:48.498551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:48.597573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:48.699784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:48.811725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:48.916669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:49.020889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:49.125092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:49.239225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:49.343289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:49.536820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:49.642770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:49.730647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:49.857471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:49.987743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:50.092943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:50.187971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:50.287820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:50.390513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:50.488147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:50.587149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:50.687344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:50.793248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:50.906139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:51.011448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:51.107131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:51.201968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:51.301178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:51.401484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:51.497690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:51.588371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:51.684898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:51.777460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:51.868722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:51.959930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:52.053929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:52.146033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:52.237351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:52.326908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:52.417152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:52.505578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:52.596140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:52.686527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:52.775549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:52.866653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:52.953112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:53.164032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:53.269587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:53.359307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:53.449425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:53.540301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:53.630924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:53.722120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:53.809897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:53.901118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:53.993454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:54.080336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:54.171782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:54.262794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:54.355643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:54.448543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:54.538921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:54.630290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:54.722345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:54.812529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:54.903974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:54.994846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:55.082823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:55.173040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:55.281486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:55.380437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:55.503171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:55.610858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:55.710276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:55.807383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:55.899306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:55.991166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:56.082951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:56.172245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:56.265339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:56.357734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:56.444622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:56.532814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:56.620890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:56.707188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:56.794163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:56.878738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:56.964791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:57.052632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:57.138811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:57.388807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:57.494482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:57.587720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:57.679816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:57.771277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:57.862116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:57.955574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:58.043967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:58.133239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:58.224561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:58.314647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:58.400873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:58.490883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:58.581452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:58.672541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:58.764954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:58.855602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:58.947769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:59.038146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:59.130529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:59.222816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:59.316994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-11T16:36:59.405373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-11T16:37:04.358334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-11T16:37:04.520040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-11T16:37:04.690502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-11T16:37:04.853107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-11T16:37:04.986905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-11T16:36:59.634310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-11T16:36:59.932934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

acousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamerelease_datespeechinesstempovalenceyear
00.887['Frank Sinatra']0.3191873330.20101hx7X9cMXHWJjknb9O6Ava0.00000070.9040-17.7961The September Of My Years - Live At The Sands Hotel And Casino, Las Vegas/1966 / Show 22018-05-040.0623117.1530.2392018
10.938['Frank Sinatra']0.2692368000.129019oquvXf3bc65GSqtPYA5S0.00000570.6830-18.1680It Was A Very Good Year - Live At The Sands Hotel And Casino, Las Vegas/1966 / Show 22018-05-040.057682.3320.1602018
20.881['Joni Mitchell']0.6443130930.212055qyghODi24yaDgKBI6lx00.000022110.7980-14.1181The Circle Game - Live at The 2nd Fret, Philadelphia, PA, 11/19662020-10-300.0347117.0720.4412020
30.955['Joni Mitchell']0.6272950930.184000xemFYjQNRpOlPhVaLAHa0.00016210.0986-15.5331Urge For Going - Live at The 2nd Fret, Philadelphia, PA, 11/19662020-10-300.0450115.8640.2992020
40.888['Joni Mitchell']0.5811834400.33102lm5FQJRHvc3rUN5YHpEWj0.00001560.1470-14.0871What's The Story Mr. Blue - Live at The 2nd Fret, Philadelphia, PA, 11/19662020-10-300.243088.3030.6422020
50.930['Joni Mitchell']0.4421479070.399026g4FBGTB9YEj7q4HlblFf0.00049960.9120-12.6611Brandy Eyes - Live at The 2nd Fret, Philadelphia, PA, 11/19662020-10-300.0780121.6620.5542020
60.949['Joni Mitchell']0.570641730.176005sxkljafFBW2vEnVczQy10.00000060.1470-22.6760Intro To Urge For Going - Live at The 2nd Fret, Philadelphia, PA, 11/19662020-10-300.2990135.6870.3482020
70.911['Joni Mitchell']0.5652326400.15304JyPPRoW8y6mA3XA7gKvoL0.000000100.3580-21.6060Intro To The Circle Game - Live at The 2nd Fret, Philadelphia, PA, 11/19662020-10-300.3780103.3090.4342020
80.932['Joni Mitchell']0.5982335200.21200GohHD8bn4lP83agkBvi5i0.00002360.6920-15.0780Eastern Rain - Live at The 2nd Fret, Philadelphia, PA, 11/19662020-10-300.0406107.1830.1722020
90.879['Joni Mitchell']0.3672138400.30707JnIimk8J3H1o828YLgZJT0.000000110.7300-12.4200Night In The City - Live at The 2nd Fret, Philadelphia, PA, 11/19662020-10-300.0568172.8670.2842020

Last rows

acousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamerelease_datespeechinesstempovalenceyear
267390.00516['Febration']0.6651833070.92604QRXELBqRwQQjd366OG1Do0.00428010.1240-4.5711Forever In Paradise2020-12-250.0651126.9140.12802020
267400.28500['Roger Fly']0.8172054380.51105RrQPoB3UQ4LYSi0v5O3WA0.90100080.0945-9.5251Saturday2020-12-090.0477102.2310.86402020
267410.02230['BigBankCarti']0.7961485120.66213MZ6J3lqRpSKX5lmbSaQVn0.000000110.3990-6.7931Snot Thot2020-12-310.293096.9550.46202020
267420.01350['Denis Pimenov']0.7702110690.68303kJINDNpUxwXuS2vVdCpOu0.86600050.1020-6.0070Periscope2020-12-050.0488120.0200.09712020
267430.01560['Elyamont', 'Cristina Soto']0.7081913410.849054NeJ65oDIydq9jr0b8qjg0.00000000.1460-2.5791Signals - Radio Edit2020-12-250.0457123.0520.45202020
267440.04840['Stephan F', 'YA-YA']0.6931771480.82601Cbf6PLWsL4s51eFepXx6L0.00001210.2310-2.6691Only Tonight - Radio Edit2020-12-250.0762126.0490.36102020
267450.14100['BigBankCarti', 'Keyvo400']0.5442150140.40713ASGdyWXeXsXtOIWtm0tv40.00000040.2530-12.7450LayUp2020-12-310.2330129.7500.49002020
267460.00917['DJ Combo', 'Sander-7', 'Tony T']0.7921476150.866046LhBf6TvYjZU2SMvGZAbn0.00006060.1780-5.0890The One2020-12-250.0356125.9720.18602020
267470.80600['Roger Fly']0.6712181470.589048Qj61hOdYmUCFJbpQ29Ob0.92000040.1130-12.3930Together2020-12-090.0282108.0580.71402020
267480.23900['Roger Fly']0.6771977100.460057tgYkWQTNHVFEt6xDKKZj0.89100070.2150-12.2371Improvisations2020-12-090.0258112.2080.74702020

Duplicate rows

Most frequent

acousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamerelease_datespeechinesstempovalenceyearcount
12500.969000['Schoolgirl Byebye']0.314743020.085500UsmyJDsst2xhX1ZiFF3JW0.79500090.160-15.7751Year,20152020-09-160.034269.8930.16120209
4250.004990['Armin van Buuren']0.397365000.638007tH6tGz6cQtpYReqHTlyjN0.00000020.364-7.2561A State Of Trance (ASOT 996) - Tune Of The Year 2019 Top 3, Pt. 22020-12-240.1760134.2840.67720208
6170.023600['Armin van Buuren']0.542338000.794007Kh32CyazzTdVEBXjKINVO0.00000070.148-6.4131A State Of Trance (ASOT 996) - Tune Of The Year 2019 Top 3, Pt. 12020-12-240.3030143.3770.81020208
2400.000717['Suffused', 'MSZ']0.5854573060.866007FmPp4BjDVaEwhsEO7B7Oe0.84600090.159-6.1260Year 2008 - MSZ Remix2020-01-060.0340127.0090.45020207
9870.397000['Anthem Lights']0.3901822880.455005rJw9VsPNdfnV9Ar97xZG20.00000040.125-4.2471K-LOVE Fan Awards: Songs of the Year (2015 Mash-Up)2015-03-310.0316107.0120.35820157
11550.829000['Anthem Lights']0.4202131650.2810000ohIpPn9LkKpeIqhfIU9V0.000000110.376-8.2041K-Love Fan Awards: Songs of the Year (2014 Mash-Up)2014-03-040.0334119.5150.22920147
11660.843000['The Kiboomers']0.903896550.2620058lQgf1Y5gRJRr6S0Nwp500.00000020.109-12.5281The Months of the Year - 2014 Version2015-08-010.081095.0980.68120157
3160.001570['Matias Grandom']0.6931945080.387000QXEvVJyHIQhVzci6kBULo0.58800090.108-13.8201Year 20002019-02-220.0520129.9680.28220196
4150.004400['Riff Raff']0.7731215000.859014KoDsg2EbYPgqpXSrKqB7F0.00711020.117-6.4591Rookie of the Year 20132013-06-250.0573159.9550.57720136
8750.196000['APRE']0.6512377780.890000Sbb2ST9tXiKBkQw7XUZrw0.00007200.301-4.7450Gap Year 20082019-03-290.0563149.9790.86620196